Deep learning from scratch: building with Python from first principles
Material type: TextPublication details: Sebastopol O'Reilly Media 2019 Description: xiv, 235 pISBN: 9789352139026Subject(s): Machine learning | Python (Computer program language) | Neural networks (Computer science) | Artificial intelligenceDDC classification: 006.32 Summary: Description All Indian Reprints of O'Reilly are printed in Grayscale With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch frameworkItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Book | Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.32 WEI (Browse shelf(Opens below)) | 1 | Checked out | 10/24/2021 | 001084 |
Description
All Indian Reprints of O'Reilly are printed in Grayscale
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects.
This book provides:
Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch framework
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